中国电力 ›› 2018, Vol. 51 ›› Issue (9): 59-64.DOI: 10.11930/j.issn.1004-9649.201803122

• 发电 • 上一篇    下一篇

基于GA的背压动态设定值多目标优化研究

王琦1, 曲燕1, 白建云1, 侯鹏飞1, 李永茂2, 冯赓1   

  1. 1. 山西大学 自动化系, 山西 太原 030013;
    2. 山西平朔煤矸石发电有限公司, 山西 朔州 036800
  • 收稿日期:2018-03-20 修回日期:2018-06-26 出版日期:2018-09-05 发布日期:2018-09-20
  • 作者简介:王琦(1973-),女,硕士,副教授,从事测控系统集成与优化研究,E-mail:wq288@sina.com
  • 基金资助:
    国家自然科学基金联合基金项目(U1610116);山西省科技重大专项项目(MD2016-02);山西省研究生联合培养基地人才培养项目(2018JD08)。

Multi-objective Optimization of Dynamic Back Pressure Setpoint Based on Genetic Algorithm

WANG Qi1, QU Yan1, BAI Jianyun1, HOU Pengfei1, LI Yongmao2, FENG Geng1   

  1. 1. Department of Automation, Shanxi University, Taiyuan 030013, China;
    2. Shanxi Pingshuo Gangue Power Generation Co., Ltd., Shuozhou 036800, China
  • Received:2018-03-20 Revised:2018-06-26 Online:2018-09-05 Published:2018-09-20
  • Supported by:
    This work is supported by National Natural Science Foundation of China (No.U1610116), Shanxi Science and Technology Major Project (No.MD2016-02) and Shanxi Province Postgraduate Joint Cultivation Area Talent Cultivation Project (No.2018JD08).

摘要: 为了解决中国多数空冷机组背压值设定存在的缺陷,利用某300 MW直接空冷机组运行过程中所获取的参数,使用多目标优化遗传算法(GA)建立具体的背压和空冷风机耗电量数学模型,在约束条件下求解最优背压和最小空冷风机耗电量。该背压动态设定方法,实现了空冷机组在变负荷及AGC考核条件下背压设定值的动态优化,对空冷机组运行参数的调整以及控制策略的优化有实用价值,有利于提高空冷机组的安全经济运行水平。

关键词: 火电厂, 直接空冷, 多目标优化, 遗传算法, 空冷风机耗电量, 背压设定值, 动态优化

Abstract: Aiming at existing defects in backpressure value setting of most air-cooled units in China, by taking advantage of the parameters obtained during the operation of a 300MW direct air-cooled unit, a multi-objective optimization genetic algorithm (GA) was used to establish a specific mathematical model for back pressure and air cooler fan power consumption, and then solve the optimal solution for back pressure and minimum air cooler fan power consumption under certain constraints. Through this dynamic setting method, the backpressure setting of air-cooled units are optimized dynamically under variable load and AGC conditions, which is of great practical significance for the operating parameter adjustment of air-cooled units as well as the control strategy optimizations. It is also beneficial for the safe and economical operation of air-cooled units.

Key words: thermal power plant, direct air cooling, multi-objective optimization, genetic algorithm(GA), air-cooled fan power consumption, back pressure setting, dynamic optimization

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